Distributed recursive learning for shape recognition through multiscale trees

The paper reports an efficient and fully parallel 2D shape recognition method based on the use of a multiscale tree representation of the shape boundary and recursive learning of trees. Specifically, the shape is represented by means of a tree where each node, corresponding to a boundary segment at some level of resolution, is characterized by a real vector containing curvature, length, symmetry of the boundary segment, while the nodes are connected by arcs when segments at successive levels are spatially related. The recognition procedure is formulated as a training procedure made by a Fuzzy recursive neural network followed by a testing procedure over unknown tree structured patterns. The proposed neural network model is able to facilitate the exchange of information between symbolic and sub-symbolic domains and deal with structured organization of information, that is typically required by symbolic processing.